Acoustic analysis
# print link to github repo if any
if (file.exists("./.git/config")){
config <- readLines("./.git/config")
url <- grep("url", config, value = TRUE)
url <- gsub("\\turl = |.git$", "", url)
cat("\nSource code and data found at [", url, "](", url, ")", sep = "")
}Source code and data found at https://github.com/maRce10/thick_billed_parrots_vocal_variation
# options to customize chunk outputs
knitr::opts_chunk$set(
tidy.opts = list(width.cutoff = 65),
tidy = TRUE,
message = FALSE
)
options(DT.options = list(
pageLength = 200,
scrollX = TRUE, # Horizontal scrolling
scrollY = "800px" # Fixed-height vertical scrolling
))# ggplot font size
bs <- 12
# custom functions
part_var <- function(model) {
var_components_pc1 <- as.data.frame(VarCorr(model))
total_variance <- sum(var_components_pc1$vcov)
var_components_pc1$proportion <- var_components_pc1$vcov / total_variance
return(var_components_pc1)
}
# plot variance partitioning results
graph_part_var <- function(levels,
data,
by = NULL,
response,
full = TRUE) {
if (!is.null(by)) {
if (full) {
by_levels <- c(unique(data[, by]), "full")
} else {
by_levels <- unique(data[, by])
}
} else
by_levels <- "full"
var_by_list <- lapply(by_levels, function(x) {
if (x != "full") {
formula <- paste0(response, " ~ (1 | ", paste(levels, collapse = " / "), ")")
cat(paste0(x, ":", formula), "\n")
dat <- droplevels(data[data[, by] == x, ])
} else {
dat <- droplevels(data)
if (!is.null(by)){
formula <- paste0(response, " ~ (1 | ", paste(levels, collapse = " / "), ") + (1 | ", by, ")")
} else {
formula <- paste0(response, " ~ (1 | ", paste(levels, collapse = " / "), ")")
}
cat("full:", formula, "\n")
}
model <- lmer(formula, data = dat)
var_components <- part_var(model)
var_components <- var_components[var_components$grp != "Residual", ]
if (!is.null(by)) {
var_components <- var_components[var_components$grp != by, ]
}
var_components$level <- factor(rev(levels), levels = rev(levels))
# sample sizes
var_components$n <- sapply(levels, function(y)
length(unique(dat[, names(dat) == y])))
var_components$prop <- as.character(round(var_components$proportion, 2))
var_components$prop[var_components$prop == "0"] <- "< 0.01"
var_components$prop <- paste(var_components$prop,
" (n = ",
var_components$n,
")",
sep = "")
var_components$by_levels <- x
var_components$perc <- paste(
round(var_components$proportion * 100, 0),
"% (n = ",
var_components$n,
")",
sep = ""
)
return(var_components)
})
var_by_df <- do.call(rbind, var_by_list)
var_by_df$by_levels <- factor(var_by_df$by_levels, levels = by_levels)
gg_out <- ggplot(var_by_df, aes(x = level, y = proportion, fill = level)) +
geom_bar(stat = "identity") +
theme_classic(base_size = 20) +
labs(x = "Level", y = "Explained variance (%)", fill = "Factor") +
facet_wrap( ~ by_levels, ncol = 1) +
theme(axis.text.x = element_text(
angle = 45,
vjust = 1,
hjust = 1
)) +
geom_text(aes(label = perc), vjust = -0.5, size = 5) +
scale_fill_viridis_d(
option = "G",
begin = 0.1,
end = 0.9,
guide = "none", direction = -1
) +
scale_y_continuous(labels = label_percent(), limits = c(0, max(var_by_df$proportion) + 0.16))
# Data for the nested ovals
oval_data <- data.frame(
# label = c("A", "B", "C", "D"),
x0 = c(0, 0, 0, 0, 0), # Center x position for all ovals
y0 = c(-1.7, -0.4, 0.7, 2.2, 3.3), # Center y position for all ovals
b = c(1.5, 3, 4.5, 6, 7.5), # Semi-major axis (horizontal radius)
a = c(2, 4, 6, 8, 10) + 1, # Semi-minor axis (vertical radius)
text.pos = c(-4.7, -2, 1, 3.2, 6) + 3 # Radius for each circle
)
oval_data <- oval_data[seq_along(levels), ]
oval_data$label <- rev(levels)
oval_data$size <- oval_data$a * oval_data$b
# oval_data <- oval_data[oval_data$label %in% c("Site", "Bird"), ] # Order by y0 for correct layering
# oval_data$label <- factor(oval_data$label, levels = rev(oval_data$label))
oval_data$label <- factor(oval_data$label, levels = oval_data$label[order(oval_data$size, decreasing = FALSE)])
oval_data$label.color <- viridis::mako(nrow(oval_data), begin = 0.1, end = 0.9, direction = -1, alpha = 1)
oval_data$label.color <- factor(oval_data$label.color, levels = oval_data$label.color[order(oval_data$size, decreasing = TRUE)])
# oval_data <- oval_data[1:4,]
# Plot using ggplot2 and ggforce
gg_oval <- ggplot(oval_data, group = label) +
geom_ellipse(aes(x0 = x0, y0 = y0, a = a, b = b, angle = 0, fill = label.color), color = "white") +
geom_text(aes(x = x0, y = text.pos, label = label), size = 7, color = c(rep("black", nrow(oval_data) - 1), "white")) +
# scale_fill_viridis_d(option = "G", begin = 0.1, end = 0.9, guide = "none", direction = 1) + # Add labels in the center
scale_fill_identity() + # Use the colors as specified in the data
theme_void() + # Remove background, axes, and grid
theme(legend.position = "none") +
coord_fixed() #+ # Keep aspect ratio fixed for true ovals
#ylim(c(-12, 6))
print(gg_oval)
return(gg_out)
}flowchart LR A(Read data) --> B(Plot acoustic space) B --> C(Regression models<br>to partition variance) C --> D(Plot variance partition) style A fill:#44015466 style B fill:#3E4A894D style C fill:#26828E4D style D fill:#6DCD594D
# knitr is require for creating html/pdf/word reports formatR is
# used for soft-wrapping code
# install/ load packages
sketchy::load_packages(packages = c("knitr", "formatR", "warbleR",
"ggplot2", "maRce10/PhenotypeSpace", "lme4", "scales", "Rtsne",
"umap", "ggforce", "DT"))tbp_calls <- readRDS("./data/processed/extended_selection_table_tpb_calls.rds")
tbp_mfcc <- readRDS("./data/processed/mfcc_tpb_calls.rds")
datatable(tbp_calls)# pca
pca <- prcomp(tbp_mfcc[, -c(1:2)], center = TRUE, scale. = TRUE)
tbp_calls$PC1.mfcc <- pca$x[, 1]
tbp_calls$PC2.mfcc <- pca$x[, 2]
ss_type <- space_similarity(type ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(tbp_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(tbp_calls, aes(x = PC1.mfcc, y = PC2.mfcc, color = type)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Call type") + theme_classic(base_size = bs) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
PCA is computed separately for each call type
tbp_calls$PC1.mfcc <- NA
tbp_calls$PC2.mfcc <- NA
for (i in unique(tbp_calls$type)) {
pca <- prcomp(tbp_mfcc[tbp_calls$type == i, -c(1:2)], center = TRUE,
scale. = TRUE)
tbp_calls$PC1.mfcc[tbp_calls$type == i] <- pca$x[, 1]
tbp_calls$PC2.mfcc[tbp_calls$type == i] <- pca$x[, 2]
# umap_l <- umap(tbp_mfcc[tbp_calls$type == i, -c(1:2)])
# tbp_calls$PC1.mfcc[tbp_calls$type == i] <- umap_l$layout[,
# 1] tbp_calls$PC2.mfcc[tbp_calls$type == i] <-
# umap_l$layout[, 2]
}
ss_type_barks <- space_similarity(Site ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(tbp_calls[tbp_calls$type ==
"barks", ]), method = "density.overlap")
ss_type_laughs <- space_similarity(Site ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(tbp_calls[tbp_calls$type ==
"laughs", ]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_barks$mean.overlap, ss_type_laughs$mean.overlap))
ggplot(tbp_calls, aes(x = PC1.mfcc, y = PC2.mfcc, color = Site)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Site") + theme_classic(base_size = bs) + facet_grid(~type,
scales = "free") + labs(title = paste0("Mean density overlap:",
round(mean_ovlp, 2)), x = "PC1", y = "PC2")
mod_barks <- lmer(PC1.mfcc ~ (1 | Site/UniqueBird), data = tbp_calls[tbp_calls$type ==
"barks", ])
graph_part_var(levels = c("Site", "UniqueBird"), data = as.data.frame(tbp_calls),
by = "type", response = "PC1.mfcc", full = FALSE)laughs:PC1.mfcc ~ (1 | Site / UniqueBird) barks:PC1.mfcc ~ (1 | Site / UniqueBird) Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.
yna_mfcc <- readRDS("./data/processed/mfcc_yna_calls.rds")
yna_calls <- readRDS("./data/processed/extended_selection_table_yna_calls.rds")
datatable(yna_calls)# pca
pca <- prcomp(yna_mfcc[, -c(1:2)], center = TRUE, scale. = TRUE)
yna_calls$PC1.mfcc <- pca$x[, 1]
yna_calls$PC2.mfcc <- pca$x[, 2]
ss_type <- space_similarity(type ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(yna_calls),
method = "density.overlap")Warning: data contain duplicated points
Warning: data contain duplicated points# graph bidimensional space with gpplot coloring by type
ggplot(yna_calls, aes(x = PC1.mfcc, y = PC2.mfcc, color = type)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Call type") + theme_classic(base_size = bs) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
PCA is computed separately for each call type
yna_calls$PC1.mfcc <- NA
yna_calls$PC2.mfcc <- NA
for (i in unique(yna_calls$type)) {
pca <- prcomp(yna_mfcc[yna_calls$type == i, -c(1:2)], center = TRUE,
scale. = TRUE)
yna_calls$PC1.mfcc[yna_calls$type == i] <- pca$x[, 1]
yna_calls$PC2.mfcc[yna_calls$type == i] <- pca$x[, 2]
# umap_l <- umap(yna_mfcc[yna_calls$type == i, -c(1:2)])
# yna_calls$PC1.mfcc[yna_calls$type == i] <- umap_l$layout[,
# 1] yna_calls$PC2.mfcc[yna_calls$type == i] <-
# umap_l$layout[, 2]
}
yna_calls <- yna_calls[!is.na(yna_calls$Site), ]
ss_type_1994 <- space_similarity(Site ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(yna_calls[yna_calls$type ==
"1994", ]), method = "density.overlap")
ss_type_2005 <- space_similarity(Site ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(yna_calls[yna_calls$type ==
"2005", ]), method = "density.overlap")Warning: data contain duplicated points
Warning: data contain duplicated pointsmean_ovlp <- mean(c(ss_type_1994$mean.overlap, ss_type_2005$mean.overlap))
ggplot(yna_calls, aes(x = PC1.mfcc, y = PC2.mfcc, color = Site)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Site") + theme_classic(base_size = bs) + facet_grid(~type,
scales = "free") + labs(title = paste0("Mean density overlap:",
round(mean_ovlp, 2)), x = "PC1", y = "PC2")
graph_part_var(levels = c("Dialect", "Site", "UniqueBird"), data = as.data.frame(yna_calls),
by = "type", response = "PC1.mfcc", full = TRUE)1994:PC1.mfcc ~ (1 | Dialect / Site / UniqueBird)
2005:PC1.mfcc ~ (1 | Dialect / Site / UniqueBird) full: PC1.mfcc ~ (1 | Dialect / Site / UniqueBird) + (1 | type) 
mpa_mfcc <- readRDS("./data/processed/mfcc_mpa_calls.rds")
mpa_calls <- readRDS("./data/processed/monk_parakeet_contactCalls_rangeComparison_extSelTable.RDS")
datatable(mpa_calls)mpa_calls$country.region <- paste(mpa_calls$country, mpa_calls$region,
sep = "-")
# pca
pca <- prcomp(mpa_mfcc[, -c(1:2)], center = TRUE, scale. = TRUE)
mpa_calls$PC1.mfcc <- pca$x[, 1]
mpa_calls$PC2.mfcc <- pca$x[, 2]
ss_type <- space_similarity(country ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(mpa_calls),
method = "density.overlap")Warning: data contain duplicated points
Warning: data contain duplicated points
Warning: data contain duplicated points# graph bidimensional space with gpplot coloring by type
ggplot(mpa_calls, aes(x = PC1.mfcc, y = PC2.mfcc, color = country)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Country") + theme_classic(base_size = bs) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
PCA is computed separately for each call type
mpa_calls$PC1.mfcc <- NA
mpa_calls$PC2.mfcc <- NA
for (i in unique(mpa_calls$country)) {
pca <- prcomp(mpa_mfcc[mpa_calls$country == i, -c(1:2)], center = TRUE,
scale. = TRUE)
mpa_calls$PC1.mfcc[mpa_calls$country == i] <- pca$x[, 1]
mpa_calls$PC2.mfcc[mpa_calls$country == i] <- pca$x[, 2]
# umap_l <- umap(mpa_mfcc[mpa_calls$type == i, -c(1:2)])
# mpa_calls$PC1.mfcc[mpa_calls$type == i] <- umap_l$layout[,
# 1] mpa_calls$PC2.mfcc[mpa_calls$type == i] <-
# umap_l$layout[, 2]
}
ss_type_us <- space_similarity(country.region ~ PC1.mfcc + PC2.mfcc,
data = as.data.frame(mpa_calls[mpa_calls$country == "United States",
]), method = "density.overlap")Warning: data contain duplicated points
Warning: data contain duplicated points
Warning: data contain duplicated pointsss_type_Uruguay <- space_similarity(country.region ~ PC1.mfcc + PC2.mfcc,
data = as.data.frame(mpa_calls[mpa_calls$country == "Uruguay",
]), method = "density.overlap")Warning: data contain duplicated points
Warning: data contain duplicated pointsmean_ovlp <- mean(c(ss_type_us$mean.overlap, ss_type_Uruguay$mean.overlap))
ggplot(mpa_calls, aes(x = PC1.mfcc, y = PC2.mfcc, color = country.region)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "country.region") + theme_classic(base_size = bs) +
facet_grid(~country, scales = "free") + labs(title = paste0("Mean density overlap: ",
round(mean_ovlp, 2)), x = "PC1", y = "PC2")
mpa_calls <- as.data.frame(mpa_calls)
graph_part_var(levels = c("country", "region", "dept_state", "site",
"Bird_ID"), data = mpa_calls[!is.na(mpa_calls$Bird_ID), ], response = "PC1.mfcc",
full = TRUE)full: PC1.mfcc ~ (1 | country / region / dept_state / site / Bird_ID) 
graph_part_var(levels = c("region", "dept_state", "site", "Bird_ID"),
data = mpa_calls[!is.na(mpa_calls$Bird_ID), ], response = "PC1.mfcc",
full = TRUE)full: PC1.mfcc ~ (1 | region / dept_state / site / Bird_ID) Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
unable to evaluate scaled gradientWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge: degenerate Hessian with 1 negative eigenvalues
graph_part_var(levels = c("region", "site", "Bird_ID"), data = mpa_calls[!is.na(mpa_calls$Bird_ID),
], response = "PC1.mfcc", full = TRUE)full: PC1.mfcc ~ (1 | region / site / Bird_ID) 
graph_part_var(levels = c("region", "site"), data = mpa_calls, response = "PC1.mfcc",
full = TRUE, by = "range")Native:PC1.mfcc ~ (1 | region / site) Introduced:PC1.mfcc ~ (1 | region / site)
full: PC1.mfcc ~ (1 | region / site) + (1 | range) 
graph_part_var(levels = c("region", "dept_state", "site"), data = as.data.frame(mpa_calls),
response = "PC1.mfcc", full = TRUE, by = "range")Native:PC1.mfcc ~ (1 | region / dept_state / site) Introduced:PC1.mfcc ~ (1 | region / dept_state / site)
full: PC1.mfcc ~ (1 | region / dept_state / site) + (1 | range) 
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.5.0 (2025-04-11)
os Ubuntu 22.04.5 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/Costa_Rica
date 2025-07-28
pandoc 3.2 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/tools/x86_64/ (via rmarkdown)
quarto 1.7.31 @ /usr/local/bin/quarto
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
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askpass 1.2.1 2024-10-04 [3] CRAN (R 4.5.0)
bitops 1.0-9 2024-10-03 [3] CRAN (R 4.5.0)
boot 1.3-31 2024-08-28 [4] CRAN (R 4.4.2)
brio 1.1.5 2024-04-24 [3] CRAN (R 4.5.0)
bslib 0.9.0 2025-01-30 [1] CRAN (R 4.5.0)
cachem 1.1.0 2024-05-16 [1] CRAN (R 4.5.0)
class 7.3-23 2025-01-01 [4] CRAN (R 4.4.2)
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cli 3.6.5 2025-04-23 [1] CRAN (R 4.5.0)
cluster 2.1.8.1 2025-03-12 [4] CRAN (R 4.4.3)
codetools 0.2-20 2024-03-31 [4] CRAN (R 4.5.0)
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crosstalk 1.2.1 2023-11-23 [3] CRAN (R 4.5.0)
curl 6.4.0 2025-06-22 [1] CRAN (R 4.5.0)
DBI 1.2.3 2024-06-02 [1] CRAN (R 4.5.0)
deldir 2.0-4 2024-02-28 [1] CRAN (R 4.5.0)
devtools 2.4.5 2022-10-11 [1] CRAN (R 4.5.0)
digest 0.6.37 2024-08-19 [1] CRAN (R 4.5.0)
dplyr 1.1.4 2023-11-17 [1] CRAN (R 4.5.0)
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magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.5.0)
MASS 7.3-65 2025-02-28 [4] CRAN (R 4.4.3)
Matrix * 1.7-3 2025-03-11 [4] CRAN (R 4.4.3)
memoise 2.0.1 2021-11-26 [3] CRAN (R 4.1.2)
mgcv 1.9-3 2025-04-04 [4] CRAN (R 4.5.0)
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miniUI 0.1.2 2025-04-17 [3] CRAN (R 4.5.0)
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NatureSounds * 1.0.5 2025-01-17 [1] CRAN (R 4.5.0)
nicheROVER 1.1.2 2023-10-13 [1] CRAN (R 4.5.0)
nlme 3.1-168 2025-03-31 [4] CRAN (R 4.4.3)
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packrat 0.9.2 2023-09-05 [1] CRAN (R 4.5.0)
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permute 0.9-8 2025-06-25 [1] CRAN (R 4.5.0)
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pillar 1.11.0 2025-07-04 [1] CRAN (R 4.5.0)
pkgbuild 1.4.8 2025-05-26 [1] CRAN (R 4.5.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.5.0)
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Rcpp 1.1.0 2025-07-02 [1] CRAN (R 4.5.0)
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[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library
* ── Packages attached to the search path.
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